Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules

Abstract

In this current work, Weighted Bayesian Association rules using the Fuzzy set theory are proposed with the new concept of Fuzzy Weighted Bayesian Association Rules to design and develop a Clinical Decision Support System on the Bayesian Belief Network, which is an appropriate area to work in Clinical Domain as it has a higher degree of unpredictability and causality. Weighted Bayesian Association rules to construct a Bayesian network are already proposed. A "Sharp boundary" issue related to quantitative attribute domains may cause erroneous predictions in medicine and treatment in the medical environment. So to eradicate sharp boundary problems in the medical field, the fuzzy theory is applied in attributes to deal with real-life situations. A new algorithm is designed and implemented in this paper to set up a new Bayesian belief network using the concept of Fuzzy Weighted Association rule mining under the Predictive Modeling paradigm named Fuzzy weighted Bayesian belief network using numerous clinical datasets with outshone results.

A Comprehensive Methodology for CNN Based Fault Identification in Induction Motors – A Case Study for EV’s

“…The feed-forward neural network using the Levenberg-Marquardt algorithm and support vector machine with recursive undecimated wavelet packet transform is also used for induction motor fault diagnosis. Fuzzy system and Bayesian theory are utilized in machine health monitoring in [27], [28], [29] . However, most of these approaches are based on supervised learning, which requires high-quality training data with good coverage of true failure conditions.…”